Overview

Dataset statistics

Number of variables21
Number of observations31781
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.3 MiB
Average record size in memory770.4 B

Variable types

Numeric10
Categorical10
Boolean1

Warnings

pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdays and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with emp.var.rate and 2 other fieldsHigh correlation
euribor3m is highly correlated with emp.var.rate and 2 other fieldsHigh correlation
nr.employed is highly correlated with previous and 3 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with emp.var.rateHigh correlation
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with emp.var.rateHigh correlation
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
education is highly correlated with jobHigh correlation
poutcome is highly correlated with pdays and 6 other fieldsHigh correlation
loan is highly correlated with housingHigh correlation
pdays is highly correlated with poutcome and 4 other fieldsHigh correlation
cons.conf.idx is highly correlated with poutcome and 7 other fieldsHigh correlation
housing is highly correlated with loanHigh correlation
contact is highly correlated with cons.conf.idx and 5 other fieldsHigh correlation
month is highly correlated with cons.conf.idx and 5 other fieldsHigh correlation
emp.var.rate is highly correlated with poutcome and 6 other fieldsHigh correlation
job is highly correlated with education and 1 other fieldsHigh correlation
cons.price.idx is highly correlated with poutcome and 6 other fieldsHigh correlation
previous is highly correlated with poutcome and 1 other fieldsHigh correlation
euribor3m is highly correlated with poutcome and 8 other fieldsHigh correlation
nr.employed is highly correlated with poutcome and 8 other fieldsHigh correlation
y is highly correlated with euribor3m and 1 other fieldsHigh correlation
age is highly correlated with jobHigh correlation
loan is highly correlated with housingHigh correlation
housing is highly correlated with loanHigh correlation
contact is highly correlated with monthHigh correlation
month is highly correlated with contactHigh correlation
df_index has unique values Unique
previous has 27268 (85.8%) zeros Zeros

Reproduction

Analysis started2021-07-01 01:36:40.128079
Analysis finished2021-07-01 01:37:07.820111
Duration27.69 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct31781
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16281.02316
Minimum0
Maximum32949
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size248.4 KiB
2021-06-30T21:37:07.990278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1590
Q18020
median16179
Q324501
95-th percentile31250
Maximum32949
Range32949
Interquartile range (IQR)16481

Descriptive statistics

Standard deviation9514.128669
Coefficient of variation (CV)0.5843692117
Kurtosis-1.200075496
Mean16281.02316
Median Absolute Deviation (MAD)8236
Skewness0.02410467435
Sum517427197
Variance90518644.34
MonotonicityStrictly increasing
2021-06-30T21:37:08.175574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
259261
 
< 0.1%
54161
 
< 0.1%
74651
 
< 0.1%
13221
 
< 0.1%
33711
 
< 0.1%
136121
 
< 0.1%
156611
 
< 0.1%
95181
 
< 0.1%
115671
 
< 0.1%
Other values (31771)31771
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
329491
< 0.1%
329481
< 0.1%
329471
< 0.1%
329461
< 0.1%
329441
< 0.1%
329431
< 0.1%
329421
< 0.1%
329411
< 0.1%
329401
< 0.1%
329391
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct76
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.11510022
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size248.4 KiB
2021-06-30T21:37:08.349150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.44031097
Coefficient of variation (CV)0.260258878
Kurtosis0.7383695914
Mean40.11510022
Median Absolute Deviation (MAD)7
Skewness0.768297312
Sum1274898
Variance109.0000932
MonotonicityNot monotonic
2021-06-30T21:37:08.537374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311472
 
4.6%
331437
 
4.5%
321412
 
4.4%
351364
 
4.3%
361338
 
4.2%
341317
 
4.1%
301303
 
4.1%
371147
 
3.6%
391126
 
3.5%
291099
 
3.5%
Other values (66)18766
59.0%
ValueCountFrequency (%)
173
 
< 0.1%
1823
 
0.1%
1936
 
0.1%
2049
 
0.2%
2181
 
0.3%
22104
 
0.3%
23167
 
0.5%
24343
1.1%
25476
1.5%
26545
1.7%
ValueCountFrequency (%)
981
 
< 0.1%
941
 
< 0.1%
924
 
< 0.1%
912
 
< 0.1%
891
 
< 0.1%
8816
0.1%
867
< 0.1%
8513
< 0.1%
846
 
< 0.1%
8313
< 0.1%

job
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
admin.
8045 
blue-collar
7097 
technician
5122 
services
3065 
management
2285 
Other values (7)
6167 

Length

Max length13
Median length10
Mean length8.954532582
Min length6

Characters and Unicode

Total characters284584
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtechnician
2nd rowservices
3rd rowretired
4th rowblue-collar
5th rowtechnician

Common Values

ValueCountFrequency (%)
admin.8045
25.3%
blue-collar7097
22.3%
technician5122
16.1%
services3065
 
9.6%
management2285
 
7.2%
retired1341
 
4.2%
entrepreneur1131
 
3.6%
self-employed1128
 
3.5%
unemployed817
 
2.6%
housemaid807
 
2.5%
Other values (2)943
 
3.0%

Length

2021-06-30T21:37:08.838240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin8045
25.3%
blue-collar7097
22.3%
technician5122
16.1%
services3065
 
9.6%
management2285
 
7.2%
retired1341
 
4.2%
entrepreneur1131
 
3.6%
self-employed1128
 
3.5%
unemployed817
 
2.6%
housemaid807
 
2.5%
Other values (2)943
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e36645
12.9%
n27377
 
9.6%
a25641
 
9.0%
l24364
 
8.6%
i23502
 
8.3%
c20406
 
7.2%
r16237
 
5.7%
m15367
 
5.4%
d12833
 
4.5%
t11269
 
4.0%
Other values (14)70943
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter268314
94.3%
Dash Punctuation8225
 
2.9%
Other Punctuation8045
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e36645
13.7%
n27377
10.2%
a25641
9.6%
l24364
9.1%
i23502
8.8%
c20406
 
7.6%
r16237
 
6.1%
m15367
 
5.7%
d12833
 
4.8%
t11269
 
4.2%
Other values (12)54673
20.4%
Dash Punctuation
ValueCountFrequency (%)
-8225
100.0%
Other Punctuation
ValueCountFrequency (%)
.8045
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin268314
94.3%
Common16270
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e36645
13.7%
n27377
10.2%
a25641
9.6%
l24364
9.1%
i23502
8.8%
c20406
 
7.6%
r16237
 
6.1%
m15367
 
5.7%
d12833
 
4.8%
t11269
 
4.2%
Other values (12)54673
20.4%
Common
ValueCountFrequency (%)
-8225
50.6%
.8045
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII284584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e36645
12.9%
n27377
 
9.6%
a25641
 
9.0%
l24364
 
8.6%
i23502
 
8.3%
c20406
 
7.2%
r16237
 
5.7%
m15367
 
5.4%
d12833
 
4.5%
t11269
 
4.0%
Other values (14)70943
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
married
19255 
single
8881 
divorced
3583 
unknown
 
62

Length

Max length8
Median length7
Mean length6.833296624
Min length6

Characters and Unicode

Total characters217169
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowsingle
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married19255
60.6%
single8881
27.9%
divorced3583
 
11.3%
unknown62
 
0.2%

Length

2021-06-30T21:37:09.149676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:09.824357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
married19255
60.6%
single8881
27.9%
divorced3583
 
11.3%
unknown62
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r42093
19.4%
i31719
14.6%
e31719
14.6%
d26421
12.2%
m19255
8.9%
a19255
8.9%
n9067
 
4.2%
s8881
 
4.1%
g8881
 
4.1%
l8881
 
4.1%
Other values (6)10997
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter217169
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r42093
19.4%
i31719
14.6%
e31719
14.6%
d26421
12.2%
m19255
8.9%
a19255
8.9%
n9067
 
4.2%
s8881
 
4.1%
g8881
 
4.1%
l8881
 
4.1%
Other values (6)10997
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin217169
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r42093
19.4%
i31719
14.6%
e31719
14.6%
d26421
12.2%
m19255
8.9%
a19255
8.9%
n9067
 
4.2%
s8881
 
4.1%
g8881
 
4.1%
l8881
 
4.1%
Other values (6)10997
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII217169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r42093
19.4%
i31719
14.6%
e31719
14.6%
d26421
12.2%
m19255
8.9%
a19255
8.9%
n9067
 
4.2%
s8881
 
4.1%
g8881
 
4.1%
l8881
 
4.1%
Other values (6)10997
 
5.1%

education
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
university.degree
9391 
high.school
7317 
basic.9y
4672 
professional.course
4025 
basic.4y
3200 
Other values (3)
3176 

Length

Max length19
Median length11
Mean length12.70148831
Min length7

Characters and Unicode

Total characters403666
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.9y
2nd rowhigh.school
3rd rowunknown
4th rowbasic.9y
5th rowuniversity.degree

Common Values

ValueCountFrequency (%)
university.degree9391
29.5%
high.school7317
23.0%
basic.9y4672
14.7%
professional.course4025
12.7%
basic.4y3200
 
10.1%
basic.6y1813
 
5.7%
unknown1351
 
4.3%
illiterate12
 
< 0.1%

Length

2021-06-30T21:37:10.010201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:10.083276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree9391
29.5%
high.school7317
23.0%
basic.9y4672
14.7%
professional.course4025
12.7%
basic.4y3200
 
10.1%
basic.6y1813
 
5.7%
unknown1351
 
4.3%
illiterate12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e45638
 
11.3%
i39833
 
9.9%
s38468
 
9.5%
.30418
 
7.5%
o28060
 
7.0%
r26844
 
6.7%
h21951
 
5.4%
c21027
 
5.2%
y19076
 
4.7%
n17469
 
4.3%
Other values (15)114882
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter363563
90.1%
Other Punctuation30418
 
7.5%
Decimal Number9685
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e45638
12.6%
i39833
11.0%
s38468
10.6%
o28060
 
7.7%
r26844
 
7.4%
h21951
 
6.0%
c21027
 
5.8%
y19076
 
5.2%
n17469
 
4.8%
g16708
 
4.6%
Other values (11)88489
24.3%
Decimal Number
ValueCountFrequency (%)
94672
48.2%
43200
33.0%
61813
 
18.7%
Other Punctuation
ValueCountFrequency (%)
.30418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin363563
90.1%
Common40103
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e45638
12.6%
i39833
11.0%
s38468
10.6%
o28060
 
7.7%
r26844
 
7.4%
h21951
 
6.0%
c21027
 
5.8%
y19076
 
5.2%
n17469
 
4.8%
g16708
 
4.6%
Other values (11)88489
24.3%
Common
ValueCountFrequency (%)
.30418
75.8%
94672
 
11.7%
43200
 
8.0%
61813
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII403666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e45638
 
11.3%
i39833
 
9.9%
s38468
 
9.5%
.30418
 
7.5%
o28060
 
7.0%
r26844
 
6.7%
h21951
 
5.4%
c21027
 
5.2%
y19076
 
4.7%
n17469
 
4.3%
Other values (15)114882
28.5%

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
no
25197 
unknown
6581 
yes
 
3

Length

Max length7
Median length2
Mean length3.035461439
Min length2

Characters and Unicode

Total characters96470
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowunknown
3rd rowno
4th rowunknown
5th rowno

Common Values

ValueCountFrequency (%)
no25197
79.3%
unknown6581
 
20.7%
yes3
 
< 0.1%

Length

2021-06-30T21:37:10.292946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:10.355317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no25197
79.3%
unknown6581
 
20.7%
yes3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n44940
46.6%
o31778
32.9%
u6581
 
6.8%
k6581
 
6.8%
w6581
 
6.8%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter96470
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n44940
46.6%
o31778
32.9%
u6581
 
6.8%
k6581
 
6.8%
w6581
 
6.8%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin96470
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n44940
46.6%
o31778
32.9%
u6581
 
6.8%
k6581
 
6.8%
w6581
 
6.8%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n44940
46.6%
o31778
32.9%
u6581
 
6.8%
k6581
 
6.8%
w6581
 
6.8%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

housing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
yes
16635 
no
14365 
unknown
 
781

Length

Max length7
Median length3
Mean length2.646298103
Min length2

Characters and Unicode

Total characters84102
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowyes
4th rowyes
5th rowno

Common Values

ValueCountFrequency (%)
yes16635
52.3%
no14365
45.2%
unknown781
 
2.5%

Length

2021-06-30T21:37:10.558618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:10.624967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
yes16635
52.3%
no14365
45.2%
unknown781
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n16708
19.9%
y16635
19.8%
e16635
19.8%
s16635
19.8%
o15146
18.0%
u781
 
0.9%
k781
 
0.9%
w781
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter84102
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n16708
19.9%
y16635
19.8%
e16635
19.8%
s16635
19.8%
o15146
18.0%
u781
 
0.9%
k781
 
0.9%
w781
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin84102
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n16708
19.9%
y16635
19.8%
e16635
19.8%
s16635
19.8%
o15146
18.0%
u781
 
0.9%
k781
 
0.9%
w781
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII84102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n16708
19.9%
y16635
19.8%
e16635
19.8%
s16635
19.8%
o15146
18.0%
u781
 
0.9%
k781
 
0.9%
w781
 
0.9%

loan
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
no
26009 
yes
4991 
unknown
 
781

Length

Max length7
Median length2
Mean length2.279915673
Min length2

Characters and Unicode

Total characters72458
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no26009
81.8%
yes4991
 
15.7%
unknown781
 
2.5%

Length

2021-06-30T21:37:10.825553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:10.889168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no26009
81.8%
yes4991
 
15.7%
unknown781
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n28352
39.1%
o26790
37.0%
y4991
 
6.9%
e4991
 
6.9%
s4991
 
6.9%
u781
 
1.1%
k781
 
1.1%
w781
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter72458
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n28352
39.1%
o26790
37.0%
y4991
 
6.9%
e4991
 
6.9%
s4991
 
6.9%
u781
 
1.1%
k781
 
1.1%
w781
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin72458
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n28352
39.1%
o26790
37.0%
y4991
 
6.9%
e4991
 
6.9%
s4991
 
6.9%
u781
 
1.1%
k781
 
1.1%
w781
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII72458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n28352
39.1%
o26790
37.0%
y4991
 
6.9%
e4991
 
6.9%
s4991
 
6.9%
u781
 
1.1%
k781
 
1.1%
w781
 
1.1%

contact
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
cellular
19961 
telephone
11820 

Length

Max length9
Median length8
Mean length8.37192033
Min length8

Characters and Unicode

Total characters266068
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowcellular
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular19961
62.8%
telephone11820
37.2%

Length

2021-06-30T21:37:11.113127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:11.182539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular19961
62.8%
telephone11820
37.2%

Most occurring characters

ValueCountFrequency (%)
l71703
26.9%
e55421
20.8%
c19961
 
7.5%
u19961
 
7.5%
a19961
 
7.5%
r19961
 
7.5%
t11820
 
4.4%
p11820
 
4.4%
h11820
 
4.4%
o11820
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter266068
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l71703
26.9%
e55421
20.8%
c19961
 
7.5%
u19961
 
7.5%
a19961
 
7.5%
r19961
 
7.5%
t11820
 
4.4%
p11820
 
4.4%
h11820
 
4.4%
o11820
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin266068
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l71703
26.9%
e55421
20.8%
c19961
 
7.5%
u19961
 
7.5%
a19961
 
7.5%
r19961
 
7.5%
t11820
 
4.4%
p11820
 
4.4%
h11820
 
4.4%
o11820
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII266068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l71703
26.9%
e55421
20.8%
c19961
 
7.5%
u19961
 
7.5%
a19961
 
7.5%
r19961
 
7.5%
t11820
 
4.4%
p11820
 
4.4%
h11820
 
4.4%
o11820
 
4.4%

month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
may
10747 
jul
5430 
aug
4626 
jun
4139 
nov
3154 
Other values (5)
3685 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters95343
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowapr
4th rowmay
5th rowjul

Common Values

ValueCountFrequency (%)
may10747
33.8%
jul5430
17.1%
aug4626
14.6%
jun4139
 
13.0%
nov3154
 
9.9%
apr2070
 
6.5%
oct588
 
1.9%
sep464
 
1.5%
mar412
 
1.3%
dec151
 
0.5%

Length

2021-06-30T21:37:11.357564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:11.432595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
may10747
33.8%
jul5430
17.1%
aug4626
14.6%
jun4139
 
13.0%
nov3154
 
9.9%
apr2070
 
6.5%
oct588
 
1.9%
sep464
 
1.5%
mar412
 
1.3%
dec151
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a17855
18.7%
u14195
14.9%
m11159
11.7%
y10747
11.3%
j9569
10.0%
n7293
7.6%
l5430
 
5.7%
g4626
 
4.9%
o3742
 
3.9%
v3154
 
3.3%
Other values (7)7573
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter95343
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a17855
18.7%
u14195
14.9%
m11159
11.7%
y10747
11.3%
j9569
10.0%
n7293
7.6%
l5430
 
5.7%
g4626
 
4.9%
o3742
 
3.9%
v3154
 
3.3%
Other values (7)7573
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin95343
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a17855
18.7%
u14195
14.9%
m11159
11.7%
y10747
11.3%
j9569
10.0%
n7293
7.6%
l5430
 
5.7%
g4626
 
4.9%
o3742
 
3.9%
v3154
 
3.3%
Other values (7)7573
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII95343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a17855
18.7%
u14195
14.9%
m11159
11.7%
y10747
11.3%
j9569
10.0%
n7293
7.6%
l5430
 
5.7%
g4626
 
4.9%
o3742
 
3.9%
v3154
 
3.3%
Other values (7)7573
7.9%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
thu
6668 
mon
6600 
wed
6270 
tue
6164 
fri
6079 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters95343
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwed
2nd rowthu
3rd rowwed
4th rowtue
5th rowwed

Common Values

ValueCountFrequency (%)
thu6668
21.0%
mon6600
20.8%
wed6270
19.7%
tue6164
19.4%
fri6079
19.1%

Length

2021-06-30T21:37:11.650135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:11.713431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
thu6668
21.0%
mon6600
20.8%
wed6270
19.7%
tue6164
19.4%
fri6079
19.1%

Most occurring characters

ValueCountFrequency (%)
t12832
13.5%
u12832
13.5%
e12434
13.0%
h6668
7.0%
m6600
6.9%
o6600
6.9%
n6600
6.9%
w6270
6.6%
d6270
6.6%
f6079
6.4%
Other values (2)12158
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter95343
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t12832
13.5%
u12832
13.5%
e12434
13.0%
h6668
7.0%
m6600
6.9%
o6600
6.9%
n6600
6.9%
w6270
6.6%
d6270
6.6%
f6079
6.4%
Other values (2)12158
12.8%

Most occurring scripts

ValueCountFrequency (%)
Latin95343
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t12832
13.5%
u12832
13.5%
e12434
13.0%
h6668
7.0%
m6600
6.9%
o6600
6.9%
n6600
6.9%
w6270
6.6%
d6270
6.6%
f6079
6.4%
Other values (2)12158
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII95343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t12832
13.5%
u12832
13.5%
e12434
13.0%
h6668
7.0%
m6600
6.9%
o6600
6.9%
n6600
6.9%
w6270
6.6%
d6270
6.6%
f6079
6.4%
Other values (2)12158
12.8%

campaign
Real number (ℝ≥0)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.617192662
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size248.4 KiB
2021-06-30T21:37:11.805380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.841318166
Coefficient of variation (CV)1.085635845
Kurtosis37.03701854
Mean2.617192662
Median Absolute Deviation (MAD)1
Skewness4.775101062
Sum83177
Variance8.073088922
MonotonicityNot monotonic
2021-06-30T21:37:11.911974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
113269
41.8%
28264
26.0%
34151
 
13.1%
42095
 
6.6%
51276
 
4.0%
6803
 
2.5%
7502
 
1.6%
8323
 
1.0%
9212
 
0.7%
10175
 
0.6%
Other values (31)711
 
2.2%
ValueCountFrequency (%)
113269
41.8%
28264
26.0%
34151
 
13.1%
42095
 
6.6%
51276
 
4.0%
6803
 
2.5%
7502
 
1.6%
8323
 
1.0%
9212
 
0.7%
10175
 
0.6%
ValueCountFrequency (%)
561
 
< 0.1%
432
< 0.1%
422
< 0.1%
402
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
353
< 0.1%
343
< 0.1%
334
< 0.1%
324
< 0.1%

pdays
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean961.2863031
Minimum0
Maximum999
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size248.4 KiB
2021-06-30T21:37:12.025019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation189.8155439
Coefficient of variation (CV)0.1974599485
Kurtosis21.37517997
Mean961.2863031
Median Absolute Deviation (MAD)0
Skewness-4.834606347
Sum30550640
Variance36029.94072
MonotonicityNot monotonic
2021-06-30T21:37:12.126289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
99930574
96.2%
3348
 
1.1%
6335
 
1.1%
492
 
0.3%
252
 
0.2%
950
 
0.2%
745
 
0.1%
1042
 
0.1%
1240
 
0.1%
535
 
0.1%
Other values (15)168
 
0.5%
ValueCountFrequency (%)
013
 
< 0.1%
123
 
0.1%
252
 
0.2%
3348
1.1%
492
 
0.3%
535
 
0.1%
6335
1.1%
745
 
0.1%
814
 
< 0.1%
950
 
0.2%
ValueCountFrequency (%)
99930574
96.2%
271
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%
211
 
< 0.1%
192
 
< 0.1%
185
 
< 0.1%
178
 
< 0.1%
1610
 
< 0.1%
1521
 
0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1798558887
Minimum0
Maximum6
Zeros27268
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size248.4 KiB
2021-06-30T21:37:12.214462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5024779297
Coefficient of variation (CV)2.793780806
Kurtosis18.59614723
Mean0.1798558887
Median Absolute Deviation (MAD)0
Skewness3.704641843
Sum5716
Variance0.2524840699
MonotonicityNot monotonic
2021-06-30T21:37:12.292327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
027268
85.8%
13652
 
11.5%
2614
 
1.9%
3175
 
0.6%
454
 
0.2%
513
 
< 0.1%
65
 
< 0.1%
ValueCountFrequency (%)
027268
85.8%
13652
 
11.5%
2614
 
1.9%
3175
 
0.6%
454
 
0.2%
513
 
< 0.1%
65
 
< 0.1%
ValueCountFrequency (%)
65
 
< 0.1%
513
 
< 0.1%
454
 
0.2%
3175
 
0.6%
2614
 
1.9%
13652
 
11.5%
027268
85.8%

poutcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
nonexistent
27268 
failure
3418 
success
 
1095

Length

Max length11
Median length11
Mean length10.43198767
Min length7

Characters and Unicode

Total characters331539
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent27268
85.8%
failure3418
 
10.8%
success1095
 
3.4%

Length

2021-06-30T21:37:12.494682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-30T21:37:12.563723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent27268
85.8%
failure3418
 
10.8%
success1095
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n81804
24.7%
e59049
17.8%
t54536
16.4%
i30686
 
9.3%
s30553
 
9.2%
o27268
 
8.2%
x27268
 
8.2%
u4513
 
1.4%
f3418
 
1.0%
a3418
 
1.0%
Other values (3)9026
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter331539
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n81804
24.7%
e59049
17.8%
t54536
16.4%
i30686
 
9.3%
s30553
 
9.2%
o27268
 
8.2%
x27268
 
8.2%
u4513
 
1.4%
f3418
 
1.0%
a3418
 
1.0%
Other values (3)9026
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin331539
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n81804
24.7%
e59049
17.8%
t54536
16.4%
i30686
 
9.3%
s30553
 
9.2%
o27268
 
8.2%
x27268
 
8.2%
u4513
 
1.4%
f3418
 
1.0%
a3418
 
1.0%
Other values (3)9026
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII331539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n81804
24.7%
e59049
17.8%
t54536
16.4%
i30686
 
9.3%
s30553
 
9.2%
o27268
 
8.2%
x27268
 
8.2%
u4513
 
1.4%
f3418
 
1.0%
a3418
 
1.0%
Other values (3)9026
 
2.7%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05942229634
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative13447
Negative (%)42.3%
Memory size248.4 KiB
2021-06-30T21:37:12.622907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.578632405
Coefficient of variation (CV)26.5663312
Kurtosis-1.095635253
Mean0.05942229634
Median Absolute Deviation (MAD)0.3
Skewness-0.7005239061
Sum1888.5
Variance2.49208027
MonotonicityNot monotonic
2021-06-30T21:37:12.711971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.412276
38.6%
-1.87171
22.6%
1.16058
19.1%
-0.12817
 
8.9%
-2.91289
 
4.1%
-3.4874
 
2.8%
-1.7630
 
2.0%
-1.1515
 
1.6%
-3141
 
0.4%
-0.210
 
< 0.1%
ValueCountFrequency (%)
-3.4874
 
2.8%
-3141
 
0.4%
-2.91289
 
4.1%
-1.87171
22.6%
-1.7630
 
2.0%
-1.1515
 
1.6%
-0.210
 
< 0.1%
-0.12817
 
8.9%
1.16058
19.1%
1.412276
38.6%
ValueCountFrequency (%)
1.412276
38.6%
1.16058
19.1%
-0.12817
 
8.9%
-0.210
 
< 0.1%
-1.1515
 
1.6%
-1.7630
 
2.0%
-1.87171
22.6%
-2.91289
 
4.1%
-3141
 
0.4%
-3.4874
 
2.8%

cons.price.idx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57488691
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size248.4 KiB
2021-06-30T21:37:12.801642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.649
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.5835192519
Coefficient of variation (CV)0.006235853134
Kurtosis-0.8492803378
Mean93.57488691
Median Absolute Deviation (MAD)0.45
Skewness-0.2315337177
Sum2973903.481
Variance0.3404947173
MonotonicityNot monotonic
2021-06-30T21:37:12.910678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9946058
19.1%
93.9185035
15.8%
92.8934522
14.2%
93.4443823
12.0%
94.4653418
10.8%
93.22769
8.7%
93.0751929
 
6.1%
92.201604
 
1.9%
92.963544
 
1.7%
92.431371
 
1.2%
Other values (16)2708
8.5%
ValueCountFrequency (%)
92.201604
 
1.9%
92.379220
 
0.7%
92.431371
 
1.2%
92.469141
 
0.4%
92.649283
 
0.9%
92.713141
 
0.4%
92.75610
 
< 0.1%
92.843210
 
0.7%
92.8934522
14.2%
92.963544
 
1.7%
ValueCountFrequency (%)
94.767102
 
0.3%
94.601169
 
0.5%
94.4653418
10.8%
94.215254
 
0.8%
94.199244
 
0.8%
94.055177
 
0.6%
94.027199
 
0.6%
93.9946058
19.1%
93.9185035
15.8%
93.876167
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.50062301
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative31781
Negative (%)100.0%
Memory size248.4 KiB
2021-06-30T21:37:13.007111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.662366193
Coefficient of variation (CV)-0.115118382
Kurtosis-0.3426941524
Mean-40.50062301
Median Absolute Deviation (MAD)4.4
Skewness0.3176004728
Sum-1287150.3
Variance21.73765852
MonotonicityNot monotonic
2021-06-30T21:37:13.108065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.46058
19.1%
-42.75035
15.8%
-46.24522
14.2%
-36.13823
12.0%
-41.83418
10.8%
-422769
8.7%
-47.11929
 
6.1%
-31.4604
 
1.9%
-40.8544
 
1.7%
-26.9371
 
1.2%
Other values (16)2708
8.5%
ValueCountFrequency (%)
-50.8102
 
0.3%
-50210
 
0.7%
-49.5169
 
0.5%
-47.11929
 
6.1%
-46.24522
14.2%
-45.910
 
< 0.1%
-42.75035
15.8%
-422769
8.7%
-41.83418
10.8%
-40.8544
 
1.7%
ValueCountFrequency (%)
-26.9371
 
1.2%
-29.8220
 
0.7%
-30.1283
 
0.9%
-31.4604
 
1.9%
-33141
 
0.4%
-33.6141
 
0.4%
-34.6141
 
0.4%
-34.8202
 
0.6%
-36.13823
12.0%
-36.46058
19.1%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct312
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.595823322
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size248.4 KiB
2021-06-30T21:37:13.236977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.781
Q11.334
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.627

Descriptive statistics

Standard deviation1.743983254
Coefficient of variation (CV)0.485002487
Kurtosis-1.449378046
Mean3.595823322
Median Absolute Deviation (MAD)0.108
Skewness-0.6795575967
Sum114278.861
Variance3.04147759
MonotonicityNot monotonic
2021-06-30T21:37:13.358775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572261
 
7.1%
4.9621933
 
6.1%
4.9631852
 
5.8%
4.9611460
 
4.6%
4.856942
 
3.0%
1.405922
 
2.9%
4.964853
 
2.7%
4.864816
 
2.6%
4.965792
 
2.5%
4.96788
 
2.5%
Other values (302)19162
60.3%
ValueCountFrequency (%)
0.6346
 
< 0.1%
0.63532
0.1%
0.63611
 
< 0.1%
0.6373
 
< 0.1%
0.6384
 
< 0.1%
0.63912
 
< 0.1%
0.648
 
< 0.1%
0.64231
0.1%
0.64317
0.1%
0.64428
0.1%
ValueCountFrequency (%)
5.0456
 
< 0.1%
56
 
< 0.1%
4.97127
 
0.4%
4.968751
 
2.4%
4.967492
 
1.5%
4.966459
 
1.4%
4.965792
2.5%
4.964853
2.7%
4.9631852
5.8%
4.9621933
6.1%

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5165.756257
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size248.4 KiB
2021-06-30T21:37:13.459413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.89091897
Coefficient of variation (CV)0.01411040617
Kurtosis-0.0629989876
Mean5165.756257
Median Absolute Deviation (MAD)37.1
Skewness-1.020757323
Sum164172899.6
Variance5313.086068
MonotonicityNot monotonic
2021-06-30T21:37:13.547141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.112276
38.6%
5099.16661
21.0%
51916058
19.1%
5195.82817
 
8.9%
5076.21289
 
4.1%
5017.5874
 
2.8%
4991.6630
 
2.0%
4963.6515
 
1.6%
5008.7510
 
1.6%
5023.5141
 
0.4%
ValueCountFrequency (%)
4963.6515
 
1.6%
4991.6630
 
2.0%
5008.7510
 
1.6%
5017.5874
 
2.8%
5023.5141
 
0.4%
5076.21289
 
4.1%
5099.16661
21.0%
5176.310
 
< 0.1%
51916058
19.1%
5195.82817
8.9%
ValueCountFrequency (%)
5228.112276
38.6%
5195.82817
 
8.9%
51916058
19.1%
5176.310
 
< 0.1%
5099.16661
21.0%
5076.21289
 
4.1%
5023.5141
 
0.4%
5017.5874
 
2.8%
5008.7510
 
1.6%
4991.6630
 
2.0%

y
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
False
28077 
True
3704 
ValueCountFrequency (%)
False28077
88.3%
True3704
 
11.7%
2021-06-30T21:37:13.611203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2021-06-30T21:36:55.618147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:55.765923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:55.888995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:55.995675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:56.100470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:56.214011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:56.342755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:56.448782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:56.555647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:56.671933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:56.775447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:56.899302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.023439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.136151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.256202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.383132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.506759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.615819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.723161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.829568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:57.937575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.038608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.144710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.243391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.346438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.447047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.544572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.648049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.749164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.845249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:58.943504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.046713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.154110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.252534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.351090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.454294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.551690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.649267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.745590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.841818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:36:59.949194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.057451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.166570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.266853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.377227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.481264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.583362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.688427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.793728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.899778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:00.999423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:01.466029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:01.575339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:01.672198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:01.767732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:01.866082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:01.971056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:02.070312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:02.166682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:02.260653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:02.355170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:02.483213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:02.608461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:02.736746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:02.901477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.022414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.124229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.234211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.340049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.443055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.542438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.648958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.753229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.850599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:03.949241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.049608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.146449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.246356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.342274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.443236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.540376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.640760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.742664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.851547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:04.949999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.047375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.142554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.239637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.333564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.428343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.522895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.624420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.727697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.832130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:05.943834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:06.045550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:06.155846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:06.269154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:06.379934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-30T21:37:06.486539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-30T21:37:13.686613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-30T21:37:13.842941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-30T21:37:14.003464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-30T21:37:14.206932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-30T21:37:14.463128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-30T21:37:06.904270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-30T21:37:07.426843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
0041technicianmarriedbasic.9ynononotelephonemaywed19990nonexistent1.193.994-36.44.8595191.0no
1151servicessinglehigh.schoolunknownyesnotelephonemaythu49990nonexistent1.193.994-36.44.8605191.0no
2263retiredmarriedunknownnoyesnotelephoneaprwed19990nonexistent-1.893.075-47.11.4455099.1yes
3342blue-collarmarriedbasic.9yunknownyesnocellularmaytue69990nonexistent-1.892.893-46.21.2915099.1no
4442technicianmarrieduniversity.degreenononocellularjulwed39990nonexistent1.493.918-42.74.9635228.1no
5532managementsingleuniversity.degreenoyesnocellularmarmon29991failure-1.892.843-50.01.7035099.1no
6647admin.marrieduniversity.degreenononocellularaugmon19990nonexistent1.493.444-36.14.9655228.1no
7732admin.singlehigh.schoolnoyesnotelephonejulfri39990nonexistent1.493.918-42.74.9625228.1no
8840technicianmarriedprofessional.coursenononotelephonejunfri19990nonexistent1.494.465-41.84.9675228.1no
9947servicesmarriedhigh.schoolnoyesnocellularnovwed19990nonexistent-0.193.200-42.04.1205195.8no

Last rows

df_indexagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
317713293946housemaidsinglebasic.9yunknownnonocellularmaywed29991failure-1.892.893-46.21.2815099.1no
317723294041techniciansingleuniversity.degreenoyesnocellularaugthu19990nonexistent1.493.444-36.14.9645228.1no
317733294133blue-collarmarriedbasic.9ynoyesnotelephonejunwed19990nonexistent1.494.465-41.84.9595228.1no
317743294233admin.singleuniversity.degreenoyesnocellularaugtue69990nonexistent1.493.444-36.14.9635228.1no
317753294334techniciansingleuniversity.degreenononocellularmaytue29990nonexistent-1.892.893-46.21.2665099.1no
317763294445admin.marrieduniversity.degreenoyesnocellularsepfri19992failure-3.492.379-29.80.7415017.5no
317773294648blue-collarmarriedbasic.9ynononotelephonemaymon19990nonexistent1.193.994-36.44.8585191.0yes
317783294730blue-collarmarriedbasic.9ynononotelephonenovtue19990nonexistent-0.193.200-42.04.7005195.8yes
317793294844self-employedmarriedprofessional.coursenoyesnotelephoneaugwed59990nonexistent1.493.444-36.14.9675228.1no
317803294924servicessinglehigh.schoolnononocellularjunwed19990nonexistent-2.992.963-40.81.2605076.2no